Bibliographic record
Abstract
The economic reading of extra-contractual or delictual civil liability sheds light on the preventive logic flowing through the foundations of the institution. Liability protects exclusive rights to scarce items, discourages harm, internalises externalities and thus places individuals before the total cost of their behaviour. At the same time, the institution compensates victims. However, this is not the only purpose underlying extra-contractual civil liability, for if it were, we could slide into a "deep pocket" system (in which liability would depend on the defendant's solvency). This slide could make the costs related to accidents and their prevention escalate, as they did in New Zealand in the 1970s and 1980s, and to a lesser degree in Quebec with respect to state car insurance. The fact that the foundations of extra-contractual civil liability reflect a deep preventive logic does not mean that the institution functions perfectly. Empirical studies cast doubt on the institution's success with respect to dissuasion as well as compensation. This explains the establishment of substitute institutions that are meant to be better designed for such purposes in specific contexts. All the same, it is important to have first clarified the missions of the core institution that they replace: this allows us to gain a better understanding of the use of demerit points to encourage prudent driving, no-fault liability to alleviate evidence problems for accident victims, and insurance for traffic and industrial accidents, as well as catastrophic accidents. The substitute institutions raise their own problems, in particular with respect to moral hazard, and these, too, need solutions. Once again, economic analysis shows us the functions and dangers of the corrective institutions. With respect to punitive damages, which are new to civil law systems, it shows that they do not necessarily contradict the logic of civil law and it indicates how they should be interpreted so as to be consistent with such logic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".